2023-24 Takeda Fellows: Advancing research at the intersection of AI and health
Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.
Artificial Intelligence
Deploy ML models built in Amazon SageMaker Canvas to Amazon SageMaker real-time endpoints | Amazon Web Services
Amazon SageMaker Canvas now supports deploying machine learning (ML) models to real-time inferencing endpoints, allowing you take your ML models to production and drive action based on ML-powered insights. SageMaker…
Generating opportunities with generative AI
Rama Ramakrishnan helps companies explore the promises and perils of large language models and other transformative AI technologies.
How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale | Amazon Web Services
Today, personally identifiable information (PII) is everywhere. PII is in emails, slack messages, videos, PDFs, and so on. It refers to any data or information that can be used to…
Dialogue-guided visual language processing with Amazon SageMaker JumpStart | Amazon Web Services
Visual language processing (VLP) is at the forefront of generative AI, driving advancements in multimodal learning that encompasses language intelligence, vision understanding, and processing. Combined with large language models (LLM)…
Schneider Electric leverages Retrieval Augmented LLMs on SageMaker to ensure real-time updates in their ERP systems | Amazon Web Services
This post was co-written with Anthony Medeiros, Manager of Solutions Engineering and Architecture for North America Artificial Intelligence, and Blake Santschi, Business Intelligence Manager, from Schneider Electric. Additional Schneider Electric…
Deploy and fine-tune foundation models in Amazon SageMaker JumpStart with two lines of code | Amazon Web Services
We are excited to announce a simplified version of the Amazon SageMaker JumpStart SDK that makes it straightforward to build, train, and deploy foundation models. The code for prediction is…
The brain may learn about the world the same way some computational models do
Two studies find “self-supervised” models, which learn about their environment from unlabeled data, can show activity patterns similar to those of the mammalian brain.
Accelerating AI tasks while preserving data security
The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.
New techniques efficiently accelerate sparse tensors for massive AI models
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.